library(ggplot2)
library(fivethirtyeight)

Syntax in ggplot2

There are many ways to “say” the same thing in ggplot2

ggplot(data = bechdel) +
  geom_point(mapping = aes(x = budget, y = domgross)) +
  geom_smooth(mapping = aes(x= budget, y = domgross))
## `geom_smooth()` using method = 'gam'
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).

ggplot(data = bechdel, aes(x= budget, y = domgross)) +
  geom_point() +
  geom_smooth()
## `geom_smooth()` using method = 'gam'
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).

ggplot(bechdel) + 
  geom_point(aes(x = budget, y = domgross))
## Warning: Removed 17 rows containing missing values (geom_point).

ggplot(bechdel, aes(x = budget, y = domgross)) +
  geom_point()
## Warning: Removed 17 rows containing missing values (geom_point).

ggplot(bechdel, aes(x= budget)) + 
  geom_point(aes(y = domgross))
## Warning: Removed 17 rows containing missing values (geom_point).

Override the global mappings

ggplot(data = bechdel, aes(x= budget, y = domgross)) +
  geom_point(aes(color=clean_test)) +
  geom_smooth()
## `geom_smooth()` using method = 'gam'
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).

Subsetting

The dplyr way

To use the dplyr package, we need to load it.

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Beatles data

Let’s make a toy dataset to play with.

beatles <- data.frame(
  name = c("John", "Paul", "George", "Ringo"),
  birth = c(1940, 1942, 1943, 1940), 
  instrument = c("guitar", "bass", "guitar", "drums")
)

Select

Select is a way to extract columns from dataframes

select(beatles, name, birth)

Your Turn

Select just the instrument column

select(beatles, instrument)

Base R

beatles[2,3]
## [1] bass
## Levels: bass drums guitar
beatles[ ,"birth"]
## [1] 1940 1942 1943 1940
beatles[ ,c("name","birth")]
beatles$birth
## [1] 1940 1942 1943 1940

Your turn

Select just the instrument column using brackets

beatles[ , "instrument"]
## [1] guitar bass   guitar drums 
## Levels: bass drums guitar

Select just the instrument column using a dollar sign.

beatles$instrument
## [1] guitar bass   guitar drums 
## Levels: bass drums guitar

Filter

filter(beatles, name == "George")

Quiz

x <- c(1, 2, 3, 4, 5)
x > 3
## [1] FALSE FALSE FALSE  TRUE  TRUE
x >= 3
## [1] FALSE FALSE  TRUE  TRUE  TRUE
x < 3
## [1]  TRUE  TRUE FALSE FALSE FALSE
x <= 3
## [1]  TRUE  TRUE  TRUE FALSE FALSE
x == 3
## [1] FALSE FALSE  TRUE FALSE FALSE
x != 3
## [1]  TRUE  TRUE FALSE  TRUE  TRUE
x = 3

Your turn

filter(beatles, birth==1940)

More than one condition

filter(beatles, birth==1940, instrument == "guitar")
filter(beatles, birth==1940 & instrument == "guitar")

Base R

beatles[c(FALSE,TRUE,TRUE,FALSE), ]
beatles[beatles$birth == 1940, ]

Baby names data

Names with at least 5 uses in a particular year, from the Social Security Administration.

library(babynames)

Your turn

Do you remember how to view a dataset?

filter(babynames, prop >= 0.08)
filter(babynames, name == "Sea")
filter(babynames, is.na(n))
filter(babynames, name == "Sea", sex == "F")
filter(babynames, n == 5 | n == 6, year == 1880)
filter(babynames, name %in% c("Acura", "Lexus", "Yugo"))

Saving results

filter(babynames, name %in% c("Acura", "Lexus", "Yugo"))
carnames <- filter(babynames, name %in% c("Acura", "Lexus", "Yugo"))
babynames <- filter(babynames, name %in% c("Acura", "Lexus", "Yugo")) # dangerous!
rm(babynames)
str(babynames) #phew!
## Classes 'tbl_df', 'tbl' and 'data.frame':    1858689 obs. of  5 variables:
##  $ year: num  1880 1880 1880 1880 1880 1880 1880 1880 1880 1880 ...
##  $ sex : chr  "F" "F" "F" "F" ...
##  $ name: chr  "Mary" "Anna" "Emma" "Elizabeth" ...
##  $ n   : int  7065 2604 2003 1939 1746 1578 1472 1414 1320 1288 ...
##  $ prop: num  0.0724 0.0267 0.0205 0.0199 0.0179 ...

Your turn

Try to: - Filter out the babynames data for your name. (You may want to also filter for your gender) - Assign that data its own name - Use your subsetted data to create a ggplot graphic of the popularity of your name over time

amelia <- filter(babynames, name=="Amelia" & sex == "F")

ggplot(amelia) + geom_point(aes(x=year, y=n))

ggplot(amelia) + geom_line(aes(x=year, y=n))

Steps

boys_2015 <- filter(babynames, year == 2015, sex == "M")
boys_2015 <- select(boys_2015, name, n)
boys_2015 <- arrange(boys_2015, desc(n))
boys_2015

Nested nonsense

arrange(select(filter(babynames, year == 2015, 
  sex == "M"), name, n), desc(n))

Piping

The %>% operator is the pipe. We can reorganized our code like so:

babynames %>%
  filter(year == 2015, sex == "M") %>%
  select(name, n) %>%
  arrange(desc(n))

[Tip: type Cmd Shift M to insert a pipe]

Your turn

Use %>% to write a sequence of functions that: - Filter babynames to just the girls that were born in 2015 - Select the name and n columns - Arrange the results so that the most popular names are near the top.

babynames %>%
  filter(sex=="F", year == 2015) %>%
  select(n, name) %>%
  arrange(desc(n))

Steps

boys_2015 <- filter(babynames, year == 2015, sex == "M")
boys_2015 <- select(boys_2015, name, n)
boys_2015 <- arrange(boys_2015, desc(n))
boys_2015

Nested nonsense

arrange(select(filter(babynames, year == 2015, 
  sex == "M"), name, n), desc(n))

Piping

The %>% operator is the pipe. We can reorganized our code like so:

babynames %>%
  filter(year == 2015, sex == "M") %>%
  select(name, n) %>%
  arrange(desc(n))

[Tip: type Cmd Shift M to insert a pipe]

Your turn

Use %>% to write a sequence of functions that: - Filter babynames to just the girls that were born in 2015 - Select the name and n columns - Arrange the results so that the most popular names are near the top.

babynames %>%
  filter(sex=="F", year == 2015) %>%
  select(n, name) %>%
  arrange(desc(n))

Diamonds data

Just the weird ones

diamonds %>%
  filter(y > 30 | x < 2)

Not the weird ones

diamonds %>%
  filter(y < 30 | x > 2) %>%
  ggplot() + 
  geom_point(aes(x = x, y = y))

Our syntax is getting weird! Note the %>% in the beginning, and + in the ggplot. Need to be careful